phi3-offline-dpo-lora-noise-0.0-5e-5-42
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6559
- Rewards/chosen: -0.1041
- Rewards/rejected: -0.1884
- Rewards/accuracies: 0.7341
- Rewards/margins: 0.0842
- Logps/rejected: -402.5698
- Logps/chosen: -418.9837
- Logits/rejected: 12.3730
- Logits/chosen: 14.0651
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|
0.691 | 0.1778 | 100 | 0.6815 | -0.0320 | -0.0641 | 0.5913 | 0.0320 | -390.1401 | -411.7723 | 11.8762 | 13.7530 |
0.6908 | 0.3556 | 200 | 0.6608 | -0.1168 | -0.1872 | 0.7262 | 0.0704 | -402.4569 | -420.2538 | 12.8524 | 14.4928 |
0.6936 | 0.5333 | 300 | 0.6605 | -0.0593 | -0.1315 | 0.7222 | 0.0722 | -396.8808 | -414.4962 | 12.5717 | 14.2749 |
0.6935 | 0.7111 | 400 | 0.6625 | -0.0799 | -0.1500 | 0.7460 | 0.0701 | -398.7299 | -416.5561 | 12.4923 | 14.1862 |
0.6941 | 0.8889 | 500 | 0.6554 | -0.1087 | -0.1935 | 0.7421 | 0.0847 | -403.0800 | -419.4448 | 12.3764 | 14.0679 |
Framework versions
- PEFT 0.7.1
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
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Model tree for Wenboz/phi3-offline-dpo-lora-noise-0.0-5e-5-42
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microsoft/Phi-3-mini-4k-instruct
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this model